Core Responsibilities

• ETL Engineering: Architect, deploy, and monitor high-volume ETL/ELT pipelines using cloud-native services (Azure Data Factory (ADF), AWS Glue, Databricks/Spark) and traditional tools (Informatica PowerCenter, Oracle Data Integrator (ODI).
• Database Optimization: Author and tune complex SQL objects (SPs, views). Perform execution plan analysis and optimize data structures using advanced indexing and partitioning for latency reduction.
• DBA & Support: Provide L2 assistance for DB health monitoring, security (ACLs), managing schemas via Liquibase/Flyway, and backup integrity verification on non-prod instances.
• Architecture: Implement Dimensional Models and integrate Change Data Capture (CDC) mechanisms (Debezium) for real-time data replication.
• Data Quality: Implement programmatic data validation routines and automated auditing checks using tools like Great Expectations.
• Integration: Develop API/middleware connections (REST/SOAP) and integrate with message queues (Kafka, Azure Service Bus) for BI platforms (Power BI, Tableau).

Technical Qualifications

• Experience: Minimum 2+ years in ETL development, Data Warehousing, and Data Engineering.
• Qualifications: Minimum bachelor's degree in computer science, Information Technology, Data Engineering, Software Engineering, or a related technical field.

Database Expertise (Required):

• Expert SQL proficiency (T-SQL, PL/SQL).
• Hands-on experience with RDBMS (Oracle, SQL Server, PostgreSQL) and NoSQL (MongoDB, Cassandra).
• Practical knowledge of basic DBA tasks and performance tuning concepts.
• ETL/ELT & Cloud Stack: Expert in cloud-based ETL/ELT platforms (ADF, AWS Glue, Databricks) and experience with on-prem tools (ODI, Informatica).
• Tools & Utilities: Proficiency in scripting (Python), Dimensional Modeling, version control (Git).
• Analytics: Familiarity with BI/Visualization tools (Power BI, Tableau, Grafana, Apache Superset) and data governance tools.